Meta File System for Big Data

1. A computer implemented method for modeling a Big Data dataset, the method comprising: creating non-specific representations of the Big Data dataset by representing, as objects in a computer model, non-specific representations including metaInformation, DataSet, BigData and Properties representations; wherein the non-specific representations of the Big Data dataset enables representation of unstructured big data, structured big data, and big data including unstructured big data and structured big data; creating non-specific representations of mapping of the Big Data by representing, as objects in a computer model, non-specific representations including User representations and marker representations, wherein each of the user representations includes one or more views of the Big Data dataset comprised of one or more of the marker representations, wherein each view shows a perspective of the Big Data dataset, wherein at least one of the one or more views of the Big Data dataset is enabled to represent an abstraction of the Big Data dataset, wherein the abstraction of the Big Data dataset is enabled to be metadata for mapping the information in the Big Data dataset; where the user representations are mapped to one or more marker representations and the marker representations are matched to one or more elements of the Big Data dataset; and creating non-specific representations of marker clusters, as objects in a computer model; wherein a marker cluster includes a set of markers; wherein a marker includes of a set of marks; where a mark is a property of data of the big dataset; wherein a marker is enabled to mark data in a file system plane, an object plane, and a block plane.

2. The method of claim 1 wherein the file system plane includes one or more file systems, wherein an object plane includes one or more object stores, wherein a block plane includes one or more block stores.

3. The method of claim 1 further comprising: creating non-specific representations of marks, as objects in a computer model, as non-specific representations comprising administration properties, contextual properties, and semantical properties.

4. The method of claim 3 wherein the user representations embodiments a person and the user representations assume a role property.

5. The method of claim 4 further comprising: mapping a contextual semantic domain to the users and markers.

6. The method of claim 1 wherein model enables a IT organization to charge back cost to a business unit that accesses the Big Data set.

7. The method of claim 1 wherein the model is stored in a low latency storage medium.

8. A computer program product for use in replication comprising: a non-transitory computer readable medium encoded with computer executable program code for migration of data, the code configured to enable the execution of: creating non-specific representations of the Big Data dataset by representing, as objects in a computer model, non-specific representations including metaInformation, DataSet, BigData and Properties representations; wherein the non-specific representations of the Big Data dataset enables representation of unstructured big data, structured big data, and Big Data including unstructured big data and structured big data; creating non-specific representations of mapping of the Big Data by representing, as objects in a computer model, non-specific representations including User representations and marker representations, wherein each of the user representations includes one or more views of the Big Data dataset comprised of one or more of the marker representations, wherein each view shows a perspective of the Big Data dataset, wherein at least one of the one or more views of the Big Data dataset is enabled to represent an abstraction of the Big Data dataset, wherein the abstraction of the Big Data dataset is enabled to be metadata for mapping the information in the Big Data dataset; where the user representations are mapped to one or more marker representations and the marker representations are matched to one or more elements of the Big Data dataset; and creating non-specific representations of marker clusters, as objects in a computer model wherein a marker cluster includes a set of markers; wherein a marker includes of a set of marks; where a mark is a property of data of the big dataset; wherein a marker is enabled to mark data in a file system plane, an object plane, and a block plane.

9. The program product of claim 8 wherein the markers include contextual markers, semantical markers, and administration markers.

10. The program product of claim 8 wherein the executable program code is further configured for execution of: creating non-specific representations of marks, as objects in a computer model, as non-specific representations comprising administration properties, contextual properties, and semantical properties.

11. The method of claim 10 wherein the user representations embodiments a person and the user representations assume a role property.

12. The program product of claim 11 wherein the executable program code is further configured for execution of: mapping a contextual semantic domain to the users and markers; wherein the file system plane includes one or more file systems, wherein an object plane includes one or more object stores, wherein a block plane includes one or more block stores.

13. The program product of claim 11 wherein the model enables a IT organization to charge back cost to a business unit that accesses the Big Data set.

14. The program product of claim 11 wherein the model is stored in a low latency storage medium.

15. An apparatus comprising: a processor and memory storing instructions that, when executed on the processor, cause the apparatus to: create, at a first module, non-specific representations of the Big Data dataset by representing, as objects in a computer model, non-specific representations including metaInformation, DataSet, BigData and Properties representations; wherein the non-specific representations of the Big Data dataset enables representation of unstructured big data, structured big data, and Big Data including unstructured big data and structured big data; create, at a second module, non-specific representations of mapping of the Big Data by representing, as objects in a computer model, non-specific representations including User representations and marker representations, wherein each of the user representations includes one or more views of the Big Data dataset comprised of one or more of the marker representations, wherein each view shows a perspective of the Big Data dataset, wherein at least one of the one or more views of the Big Data dataset is enabled to represent an abstraction of the Big Data dataset, wherein the abstraction of the Big Data dataset is enabled to be metadata for mapping the information in the Big Data dataset; where the user representations are mapped to one or more marker representations and the marker representations are matched to one or more elements of the Big Data dataset; and create, at a third module, non-specific representations of marker clusters, as objects in a computer model; wherein a marker cluster includes a set of markers; wherein a marker includes of a set of marks; where a mark is a property of data of the big dataset; wherein a marker is enabled to mark data in a file system plane, an object plane, and a block plane.

16. The apparatus of claim 15 wherein the markers include contextual markers, semantical markers, and administration markers.

17. The apparatus of claim 15 , wherein the instructions that, when executed on the processor, further cause the apparatus to: create, at a fourth module, non-specific representations of marks, as objects in a computer model, as non-specific representations comprising administration properties, contextual properties, and semantical properties.

18. The apparatus of claim 17 wherein the user representations embodiments a person and the user representations assume a role property.

19. The apparatus of claim 18 , wherein the instructions that, when executed on the processor, further cause the apparatus to: map a contextual semantic domain to the users and markers; wherein the file system plane includes one or more file systems, wherein an object plane includes one or more object stores, wherein a block plane includes one or more block stores.

20. The apparatus of claim 15 wherein model enables a IT organization to charge back cost to a business unit that accesses the Big Data set.

21. The computer implemented method of claim 2 wherein user representation connection to a marker denotes security and access privileges of that user representation to the data associated with the marker representation.

22. The computer implemented method of claim 2 further comprising: determining storage systems in the block plane; determining file systems in the file system plane; and determining object stores in the object plane.